System and method for guidance of anesthesia, analgesia and amnesia

ABSTRACT

Described is a system and method which includes the following steps. A plurality of interventional agents are administered to a patient until the patient attains a predetermined plane of anesthesia. Brain waves of the patient are amplified and digitized before and after the administering step to generate a first set of digital data. The brain waves of the patient are amplified and digitized during a medical procedure to generate a second set of digital data. Separate trajectories for at least two different indices of an anesthetic state of the patient are computed during the medical procedure as a function of a comparison of the first and second sets of digital data. The indices include a Depth Index (DI), a Memory Index (MI) and a Pain Index (PI). The DI corresponds to a depth of anesthesia of the patient, the PI corresponds to a sensitivity of the patient to pain and the MI corresponds to an ability of the patient to form and store memories.

PRIORITY CLAIM

This application is a Continuation of U.S. patent application Ser. No.10/279,131, entitled “System and Method for Guidance of Anesthesia,Analgesia and Amnesia” filed Oct. 23, 2002. The specification of theabove-identified parent application is incorporated herewith byreference.

FIELD OF THE INVENTION

The present invention relates to medical systems and methods and moreparticularly, to an electroencephalograph (“EEG”) based system formonitoring or automatic guidance of anesthesia, analgesia, and amnesiaduring surgical operations.

BACKGROUND INFORMATION

Anesthetic drugs which, when properly administered, induce loss ofawareness, are often used for painful and serious medical proceduressuch as surgical operations. A general anesthetic, when properlyadministered, will cause a progressive depression of the central nervoussystem so that the patient loses consciousness. A local anesthetic,however, only affects sensation at the region to which it is applied.

Generally, the patient, prior to a surgical operation, is anesthetizedby a specialized medical practitioner (“anesthesiologist”) who may be aBoard Certified physician, or a specially trained nurse anesthetist. Oneor more volatile inhalational liquids or gases may be administered(e.g., nitrous oxide, methoxy flurane, sevoflurane, isoflurane,desflurane, ethylene, cyclopropane, ether chloroform, halothane, etc.).Certain desirable anesthetic gases such as Ciboflorane® (Abbott Lab) maysometimes not be used because of their cost. Alternatively, nonvolatiledrugs may be administered by injection or intravenous infusion (e.g.,flumazenil, thiopentone, Retamine, remifentanyl, midazolam, pentothal,propofol, evipal procaine and etomidate® (Abbott)). The objectives ofgeneral anesthesia administered prior to a surgical operation, mayinclude:

-   a) blocking the patient's movements and relaxing the patient's    muscles to prevent involuntary reflex muscle movements which may    interfere with the operation;-   b) preventing the patient from being aware (i.e., loss of    consciousness, or sedation) during the operation;-   c) preventing the patient feeling pain (i.e., loss of sensation, or    analgesia) during the operation; and-   d) preventing the patient from remembering intra-operative events or    discussions (i.e., amnesia).    Furthermore, the anesthesia should not lower blood pressure to a    dangerous extent (e.g., below 50 mm Hg for mean arterial pressure    (MAP)).

These objectives of general anesthesia may often be attained by separateadministration of hypnotic or sedative, analgesic and amnesic agents, inaccordance with the clinical judgment of the managing anesthesiologistevaluating the apparent state of the patient and a variety of vitalsigns.

In order to monitor the “anesthetic depth” or “plane of anesthesia” ofthe patient, a skilled anesthesiologist looks at the vital signals ofthe patient (e.g., breathing, blood pressure, etc.) to determine ifmore, or less, anesthetic is required. Often he/she looks into thepatient's eyes to determine the extent of the dilation of the pupils asan indication of the level (or depth) of the effect of the anesthesia.Complete reliance on the availability, skill and attention of theanesthesiologist presents problems in some situations. In addition,respiration may be artificially controlled (e.g., by a respirator)and/or medications may block or alter useful autonomic signs. In theabsence of graded neurological reflexes, the depth of suppression ofbrain activity related to awareness often may not be accurately gauged.The mute, paralyzed patient cannot report the experience of pain.Furthermore, pain cannot be reliably inferred from vital signs sincethey may be blocked by the presence of medications. In some operations(e.g., heart surgery), the head is covered so that the patient's eyescannot be viewed and pupillary dilation is not apparent. No reliableestimate may then be made of the possibility that the patient may beaware of environmental events, experience pain and/or be able to storeand retrieve memories about unpleasant experiences. Furthermore, duringprolonged operations (e.g., 10 to 15 hours or more), the attention ofthe anesthesia nurse or anesthesiologist may not be constant.

Also, at times, an anesthesiologist may not be available (e.g., inemergency or battlefield situations). Similarly, in isolated geographiclocations, it may be impractical to move a patient requiring anoperation to a hospital where an anesthesiologist would be available.However, a physician or surgeon may be able to perform a requiredoperation if there were some way to effectively and safely anesthetizethe patient.

U.S. Pat. No. 2,690,178 to Bickford purports to describe an automaticsystem for applying anesthetic to a patient while monitoring thepatient's brain waves to monitor the effects of the anesthetic. Bickfordused an integrated potential output of the cortex to judge the efficacyof the anesthetic. (See also, U.S. Pat. Nos. 4,280,494 and 4,533,346 toCosgrove et al. entitled “System for Automatic Feedback-ControlledAdministration of Drugs”). The EEG measure used is an “EEG powerresponse” (i.e., a total power output of the brain). However, the use ofthe single measure of integrated cortex output as described in theBickford and Cosgrove patents may not provide a reliable control signalfor applying a general anesthetic. Different anesthetics have differentimpacts on power output and several may actually cause an increase in apower detected by a cortical EEG. Furthermore, in some instances, thenature of the power detected changes depending upon electrode position.In addition, not only do different anesthetics have different effectsupon the EEG, but those effects may vary from patient to patient as aconsequence of different pre-operative medications and/or differentbiochemical sensitivities.

The following patents which describe methods and apparatus formonitoring and/or controlling the provision of anesthetic to patientsare hereby expressly incorporated by reference: U.S. Pat. No. 6,315,736to Tsutsumi et al.; U.S. Pat. No. 6,317,627 to Ennen et al; U.S. Pat.No. 6,016,444 to E. R. John; U.S. Pat. No. 5,699,808 to E. R. John; U.S.Pat. No. 5,775,330 to Kangas et al.; U.S. Pat. No. 4,557,270 to E. R.John; U.S. Pat. No. 5,010,891 to Chamoun; and U.S. Pat. No. 4,869,264 toSilberstein.

SUMMARY OF THE INVENTION

The present invention is directed to a method for monitoringanesthetization of a patient undergoing a medical procedure, comprisingthe steps of (a) removably connecting a set of at least twoelectroencephalograph (“EEG”) electrodes to the scalp of the patient,(b) administering sufficient anesthesia to the patient so that thepatient attains a plane of anesthesia selected by an operator and ©amplifying and digitizing brain waves of the patient after step (b) andbefore beginning the medical procedure to obtain a first set of digitaldata in combination with the steps of (d) amplifying and digitizingbrain waves of the patient during the medical procedure to provide asecond set of digital data, (e) analyzing the first and second sets ofdigital data in at least one of a time domain and a frequency domain;and (f) computing from the data analysis of step (e) separatetrajectories for at least two different indices of an anesthetic stateof the patient during the medical procedure, the indices being selectedfrom a group including a Depth Index (DI), a Memory Index (MI) and aPain Index (PI), wherein the DI corresponds to a depth of anesthesia ofthe patient, PI corresponds to a sensitivity of the patient to pain andMI corresponds to an ability of the patient to form and store memories.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block schematic drawing of an exemplary apparatus accordingto the present invention.

DETAILED DESCRIPTION

The present invention utilizes electrophysiological methods to provideautomatic quantitative evaluation separately for a level of awareness(sedation), a sensitivity to pain and/or an ability to comprehendauditory speech and store memories of intraoperative or environmentalevents. This information may be provided as a monitor to aid ananesthesiologist in the management of an individual patient or may beused as an input to a servo system which automatically deliversanesthetic, analgesic and amnesic agents to optimize the state of apatient. In particular, before an operation, the anesthesiologist mayattach a plurality of removable EEG electrodes to the scalp of thepatient (preferably, two to eight electrodes and more preferably, fiveelectrodes). If five active electrodes are used, they may, for example,be placed at F1, F2, F7 and F8 active positions as would be understoodby those skilled in the art. Reference electrodes may then be placed,for example, at FPZ and CZ (vertex) and the cheekbone. In addition, anearpiece insert may be used to apply audio stimulus to the patient, anda finger cot electrode may be used to apply slight electrical shocks assomatosensory stimulus. The anesthesiologist may then administer aselected anesthetic to place the patient at a desired depth ofanesthesia using his clinical judgment, based upon the patient's vitalsigns and clinical experience. At that time, measurements of thepatient's EEG, AER (Auditory Evoked Response) and/or SER (SomatosensoryEvoked Response) may be automatically made to provide an adequateself-norm (reference or base line). Measures of vital signs such asheart rate, stroke volume, blood pressure, respiration and temperaturemay also be obtained and monitored from the anesthesiology console. Inaddition, oxygen saturation may be measured, for example, using an NIRsensor such as “INVOS”® (In Vitro Optical Spectroscopy) (Somanetics). AQEEG system, which monitors the electrophysiology of the patient, maythen be able to detect changes in the clinical state of the patient(e.g., changes in the depth of anesthesia, sensitivity to pain, orprobability of memory storage) before there are clinical or qualitativesigns of change (e.g., movement, tachycardia, or increased bloodpressure). During the operation, the QEEG system automatically andcontinually collects on-going EEG and also challenges the patient withregularly repeated periods of stimuli to provide evoked potentials, suchas AER and SER. These data are subjected to automatic artifact removaland features selected from the self-norm are continuously analyzed anddisplayed as three trajectories. In one embodiment of the invention,deviations beyond confidence limits (i.e., a reference band) for any ofthe trajectories, may automatically control the application of thedifferent agents to achieve or maintain the desired depth of anesthesia.Alternatively, this data may be displayed to the anesthesiologist whomay then make judgments based on his experience, etc., as to whatmeasures are required to optimize the anesthesia of the patient.

In accordance with one embodiment of the present invention, a Guidanceof Anesthesia, Analgesia and Amnesia System (“GAS”) includes an EEGsystem and automatic quantitative analysis of the EEG (“QEEG”) andsensory evoked responses. It serves as an intra-operative multimodalmonitor to inform the anesthesiologist of the present state ofanesthesia of the patient or, if desired, to automatically administerdosages of one or more agents during an operation to facilitatemanagement of the patient. If an anesthesiologist or intensivist is notavailable, the system may permit a physician or paramedical personnel tomanually or automatically maintain the desired level of anesthesia in apatient.

In contrast to conventional systems, the GAS separately evaluatesseveral dimensions of the state of the patient. In addition to servingas a monitor to aid optimal manual management of the patient, thepresent method and system enables an automatic control of multipledimensions of anesthesia. Separate measures quantify indices for thedepth of anesthesia (DI), sensitivity to pain (Pain Index or PI) andlikelihood of storage of Memory Index (MI).

The first dimensional measure, the Depth Index (DI), relates to a depthof anesthesia. If the patient has attained a satisfactory depth ofanesthesia, consciousness has been lost and the patient's muscles aresufficiently relaxed so that involuntary muscle movements do notinterfere with the operation. This is an over-all measurement of thedepth of anesthesia. An example of an agent primarily directed to attainand maintain general anesthesia level (DI), is propofol (“Diprivan”® byZeneca Phar). The measurement of the patient's immediate sensitivity topain is called the “Pain Index” (PI) and an example of an agentprimarily directed to controlling PI is remifentanyl (selective mu-opiodwith a very short half-life). The measurement of the functional state ofthe patient's memory is called the “Memory Index” (MI). An example of anagent directed primarily to control MI is midazolam (Versed).

General anesthetics produce a progressive depression of the centralnervous system. Generally, they produce an irregular descendingparalysis of the central nervous system and suppression of the sensorycortex. The paralysis successively affects the basal ganglia, thecerebellum and the spinal cord; without suppression of the medulla(respiratory and cardiac functions). The sensory input to the cortex issuppressed because the sensory pathway from the brain stem reticularformation and the thalamus is inhibited. The electrical activity ofevery local brain region as well as interactions among regions isauto-regulated by a complex neuroanatomical homeostatic system,producing an EEG power spectrum which is generally predictable inhealthy persons of any age and independent of ethnic background, in theabsence of perturbing illnesses or chemical substances. Serialmeasurements are extremely stable and reproducible within anyindividual. Anesthetic agents alter the relationships within thehomeostatic system, producing certain changes in the power spectrum,which have been shown to be invariant with loss of consciousness causedby any agent but reversible with the return of consciousness.Anesthetics act upon pacemaker oscillator cells which normally regulatethe stable spontaneous EEG rhythm, generating a power spectrum with apeak that is generally in the center of the Alpha band (8-12 Hz)) viathe nucleus reticularis. This inhibits the thalamus via theneurotransmitter gamma-amino butyric acid and, in effect, closes thesensory gate to the cortex. The pacemaker cells are hyperpolarized bythis inhibitory influence of n. reticularis, thereby slowing theiroscillations to produce slower Alpha waves and enhancement of Thetawaves. The slower Alpha waves and Theta waves, and other distinctivealterations of the patient's normal regional EEG power spectra, andelectrophysiological signs of interactions between regions, may bedetected by the QEEG analysis system of the present invention. Usingpattern recognition algorithms, which may be discriminant functions,quantitative features are continuously extracted from ongoing EEG dataand used to construct a scale for depth of anesthesia, the Depth Index(DI). This information may be presented to the anesthesiologist to serveas an adjunct to the manual management of the patient. Alternatively,servo systems may be used to administer appropriate agents automaticallyto control the DI.

The present invention presents a relatively simple and yet effective andreliable system and method for the monitoring and/or control of themultiple dimensions of anesthesia. The method is based upon computationof the covariance matrix of spectral quantitative EEG (QEEG) featureswithin each electrode and among a set of electrode positions. In itssimplest form, it uses a set of anterior (frontal) EEG electrodes on theforehead. When the patient attains the surgical plane of anesthesia, thepower in each band will change within each electrode and thecross-spectral matrix will change. One way to display this data may beas a scrolling waterfall of the power spectra from each lead, updated atperiodic intervals (e.g., Compressed Spectral Array or “CSA”). The meansand standard deviations of baseline samples of the covariance matricesand the measures may be used to define a self-norm. As updated samplesof EEG are analyzed, a comparison relative to population orself-normative data is made of the absolute and relative EEG powerwithin each of the electrodes continuously or within selected frequencybands, and the symmetry and coherence relationships among these spectralmeasurements within and between the set of electrodes. This comparisonpreferably entails transformation of every measure for Gaussianity andrescaling the measure to the common metric of probability by computingthe standard or Z score for each variable. A second way to display thisdata may be as a scrolling waterfall of the Z-transformed spectra orZSA. A third way to display this QEEG data may be to extract selected,differentially sensitive variables from the EEG and compute separatecomposites such as Mahalanobis distances or discriminant scores toprovide scales which accurately assess DI, PI and MI.

These scores may be displayed as separate, updated numerical values oras separate updated trajectories of the values versus elapsedintra-operative or monitoring time. If the patient begins regainingconsciousness, sensitivity to pain or the ability to comprehend speechand store memories, as shown by the trajectory for the correspondingdimension, the confidence level (mean+2 standard deviations) around theself-norm (baseline) for that dimension will be exceeded. An alarm maybe sounded or a vibratory signal transmitted. If that occurs, moreagent, directed toward the specific dimension displaying change ofstate, may then be delivered (titrated) to the patient manually by theattending medical personnel or, alternatively, the corresponding agentmay be automatically delivered via a self-adaptive servo algorithm.Conversely, if these changes are excessive, less agent will be indicatedrelative to the self-norm, and the attending personnel or the servosystem may reliably intervene to control the administration of each ofthe agents in a manner optimized for the individual patient.

The QEEG variables may be augmented by sensory evoked potentials (“EPs”)and autonomic data to obtain measurements for quantifying the pain (PI)and memory (MI) indices. To obtain the sensory EPs, the system presentsto the patient a programmed sequence of concurrent or sequentialstimulations in one or multiple sensory modalities. Preferably, twomodes are used: (1) auditory stimulation (e.g., auditory clicks orrectangular tone pips at about 65 dB, modulated at a frequency selectedto maximize EP amplitude, such as, approximately 1500 Hz), delivered tothe ears via air tubes from an audio source at an ‘auditory tracer’repetition rate F1; and (2) somatosensory stimulation consisting ofelectrical shocks (e.g., 0.2 msec pulses of constant current at about 12mA delivered to a peripheral nerve, preferably via a finger cot, at asecond ‘somatosensory tracer’ rate (F2). The tracer rates F1 and F2,although concurrent, may preferably be selected at different primenumber frequencies to permit separation of the different EP's and avoidinterference. Concurrent stimulations permit a more rapid, examinationand provide the patient's responses more quickly. However, intermittentsequential stimulation may be more effective as habituation may readilybe avoided by randomizing sequences or other maneuvers to maximize EPamplitudes. Based on the responses to the auditory stimuli, the systemtests the functional state of the lateral lemniscal auditory pathway inthe brain stem (Brain Stem Auditory Evoked Response or BAER), thethalamus (Mid-Latency Auditory Evoked Response or MLAER) and theauditory cortex (“AER”). Based on the responses to electrical stimuli,the system tests the functional state of the spinal cord, mediallemniscal pathways in the brain stem and the somatosensory cortex(Somatosensory Evoked Response, or SER).

One way to quantify the EPs is to utilize separate tracer frequencies,F1 and F2, in order detect the different times of presentation of thestimuli in the two different modalities to provide ‘trigger pulses’needed to compute the wave shape of each of the average evoked responsesin the time domain, using the conventional evoked response averagingtechniques. This selective averaging may be performed whether thestimuli are presented simultaneously or sequentially. The raw waveshapes may be optionally displayed as a scrolling waterfall, orCompressed Evoked Potential Array (CEPA). The system may extract fromeach such wave shape a numerical feature of merit or a metric (e.g.,such as the length of the curvilinear outline or the area under the EPwave shape). From a baseline sample for both the AEPs and SEPs, the meanand standard deviation of the distribution of such EP measures may bespecified. Subsequent samples may be Z-transformed to provide a commonmetric of probability. These Z-scores may be displayed as periodicallyupdating numerical values or as continuously updating trajectories. Theymay also be combined with the Z-scores of the separate QEEG measuresfound to be sensitive to pain or memory storage into a ‘State Vector’ inorder to provide a multi-modal and more sensitive and specificassessment. Such multivariate vectors may be computed as the square rootof the sum of the squared separate Z-scores. Such vectors may combineQEEG and SEP Z-scores to yield a Pain State Vector, QEEG and AEPZ-scores to yield a Memory State Vector, or QEEG, SEP and AEP Z-scoresto yield a Brain State Vector.

Another way to quantify the EPs is to perform very narrow band (e.g.,using 0.5 Hz frequency bins) FFTs on the EEG recorded in the absence ofthe tracer stimuli and during intermittent or constant periods ofstimulation. Using the very narrow band power computed over a slidingwindow of appropriate length (e.g., 20 to 60 seconds), the power in thebin corresponding to F1 F2 is divided by the mean power of the twoadjacent bins of lower frequencies and the two adjacent bins of higherfrequencies. The power of the EEG is equal to its variance because thevariance of a set of samples of a variable equals the mean squared valueminus the square of the mean value across the set and the mean value ofthe EEG is zero. Thus, this quotient of powers is equivalent to anF-ratio. In this way, without actually computing an average responsewave shape, a statistically interpretable figure of merit can be readilyprovided for the responsiveness of the patient to somatosensory orauditory stimulation. By constructing a database of such F values in abaseline sample, the updating F ratio's can be Z-transformed toprobability and processed for display on a monitor or inputs to a servocontroller just as the features extracted from the EP wave shapes.

Measures of vital signs (e.g., heart rate, stroke volume, bloodpressure, respiration and temperature) may also be obtained andmonitored from the anesthesiology console. In addition, oxygensaturation may be measured (e.g., using an NIR sensor such as “INVOS”®(In Vitro Optical Spectroscopy). A preferred comprehensive systemmonitors the electrophysiology of the patient, detecting changes inavailable measurements of such vital signs. Any such data which becomesavailable may be treated the same way in principal (i.e., a baselinesample may be collected to serve as the reference state). The quantifiedfeature(s) may then be assembled into a baseline distribution sample.After transforms for Gaussianity, if necessary, the mean and standarddeviation of each measure are calculated. Z-transformation of the rawmeasure values now rescales them all in the common metric ofprobability. These may now be presented on the screen as numericalvalues or displayed as continuous updating trajectories as univariatesor as multivariate “vital sign vectors” combined by computing the squareroot of the sum of the squared Z-scores. While a mathematically morecorrect multivariate may require correction for intercorrelations usingthe covariance among the set of measures, the simple square root of thesum of squares errs in the direction of possible over-estimation of thevector length, which acts as a ‘fail-safe’ early warning signal. Inparticular, the normalized variability in heart rate (HRV) has beenreported to be a sensitive autonomic indicator of pain. Z (HRV) mightcontribute enhanced sensitivity if incorporated into the “pain vector”together with the selected pain-sensitive QEEG variables and selectedSEP features.

As shown in FIG. 1, prior to a surgical operation, a plurality of EEGelectrodes (e.g., EEG electrodes 2 a-2 e) are removably secured to thescalp 1 of the patient. Preferably, the EEG electrodes will include thefollowing forehead locations: F1, F2, F7, F8 (all 4 active) and FPZ(reference). The capital letters refer to position location names in theInternational 10/20 Electrode Placement System as would be understood bythose of skill in the art. Additional removable electrodes may beutilized as desired while additional reference electrodes (unilateral orlinked) may be removably positioned on the patient's mastoids orearlobes (A1, A2). An electrode may be placed on the shoulder over Erb'sPoint to serve as confirmation that SEP are being conducted through thespinal cord. EOG electrodes may optionally be placed at the outercanthus of the eye to facilitate artifact rejection. As would further beunderstood by those of skill in the art, electrodes may also be placedon the central vertex (Cz) to record brainstem potentials, on the chestfor EKG recording and on the cheekbone to serve as the ground.

The electrodes 2 a-2 e preferably use a standard electrolyte gel, orother application method, for contact so that the impedances of eachelectrode-skin contact are below 5000 ohms. Alternatively, for someapplications, needle electrodes, a pre-gelled electrode appliance withadhesive or other means of fixation, or an electrode cap or net withpreviously located electrode positions may be used. The EEG system,described below, automatically checks the electrode-skin impedance ateach electrode at frequent intervals, (e.g., every minute), and displaysa warning (e.g., a red LED light) if any such impedance falls below 5000ohms.

As shown in FIG. 1, the patient's head 1 is connected to the patientmodule which includes a desired number of electrodes 2 a-2 e. FIG. 1shows four active electrodes.

Each of the electrodes 2 a-2 e is connected to a respective one of theEEG/EP amplifiers 3 a-3 e, with each electrode lead being connected toits respective amplifier. Each amplifier 3 a-3 e has an input isolationswitch, (e.g., a photo-diode and LED coupler), to prevent currentleakage to the patient. The EEG amplifiers 3 a-3 e are high-gainlow-noise amplifiers, preferably having, for example, peak-to-peak noiseof 1 microvolt or less, a frequency range of 0.5 to 200 Hz, fixed gainof 10,000, common mode rejection of 100 db or more (4 amplifiers). Twoauditory or somatosensory brainstem EP amplifiers may have, for example,a peak to peak noise of less than 1 microvolt, a frequency range from 30to 5000 Hz, gain of 100,000 (2 amplifiers) and a common mode rejectionof at least 100 dB. Alternatively, high-gain amplifiers may be used withfixed gain of, for example, 10,000 of which 2 may be remotely switchedto a fixed gain of, for example, 100,000. Amplifier parameters may beswitched for separate data collection of EEG and EP, separate amplifiersmay be connected to the same electrode input, or a programmable A/Dmultiplexer converter may be used to output the separated data.

The amplifiers 3 a-3 e are connected to a four channel analog-to-digitalmultiplexer 4 (A/D multiplexer). The multiplexer 4 samples the amplifiedanalog brain waves at a rate of, for example, 5 KHz for each channel.The multiplexer 4 is connected to “buffer signal” 5 which stores thesignal, and “buffer noise” 6 which stores samples of the “noise”, thatis, amplifier output of EEG when no stimuli are delivered to elicit EPS.The buffers 5, 6 and A/D multiplexer 4 are connected to a dedicateddigital signal processor (DSP) 7, such as, for example, model TMS320C44®(Texas Instruments). Alternatively, the DSP 7 may be a Pentium 4Processor® (Intel) or a digital signal processor such as the TMS320C44®(Texas Instruments) along with a microprocessor. The DSP may becontrolled by, for example, a software program 8 and connected, througha dedicated 512-point FFT 9 (Fast Fourier Transform) to a digital combfilter 10.

The comb filter 10 is connected to, and controls, the IFFT 11 (InverseFast Fourier Transform). The output of IFFT 11 is connected to thesystem microprocessor 12. The microprocessor 12 is also connected to,and controls, the stimulus generator 13 (e.g., lights, loudspeaker,shock, device, etc.), the mass storage 14 (e.g., a hard disk), thedisplay 15 (e.g., a CRT), a printer 16 and a keyboard operator controlpanel 17. The microprocessor 12 operates under control of a softwareprogram 18. Preferably, as shown, the stimulus generator 13 is connectedto “auditory” 20, which generates clicks at, for example, 100 dB and atan “auditory tracer” frequency “F1”. The clicks may then be transmittedto the patient via, for example, earphones or air tubes. The stimulusgenerator 13 is also connected to “somatosensory” 21 which deliverselectrical stimulation (e.g., constant current electrical shock pulses),for example, of 200 microseconds duration and 12 milliamps current. Theelectrical stimulation may be transmitted to the patient via, forexample, a fingertip cot at a second, and different, “somatosensorytracer” frequency “F2.”

The digital comb filter 10 may be as described in U.S. Pat. No.4,705,049, incorporated by reference herein. The comb filter mayconsidered a series of band pass and band stop filters which areresponsive over a selected range. The selected range may for example beis 0-3000 Hz and may, preferably be 0-1400 Hz. Preferably, band passfilters may operate at 10-580 Hz, 600-640 Hz, 720-800 Hz and 900-1400 Hzwith band-stop filters at 0-10 Hz, 580-600, 640-720 Hz, 800-900 Hz andabove 1400 Hz. Thus the band pass filters form the “teeth” of a comb andare selected to accord with frequencies in which a signal/noise ratio isacceptable. The band-stop filters are selected to remove frequencies inwhich the noise is excessive.

The multiplexer 4 may be programmed to obtain samples of the signal andof the noise. The “noise” is preferably obtained when there is anabsence of evoked potential stimuli and the “signal” is obtained duringstimulation, beginning with presentation of the stimuli or after apre-selected delay. The program 8 with its controlled DSP 7 conditionsthe input signals and insures that they are valid biological signals.Such validation checks on the input signals include periodic calibrationmeasurements and impedance measurements and continuous automaticartifact rejection algorithms. The microprocessor 12 automaticallyprovides a timed set of two kinds of stimuli for simulator 13: An audiosound from a speaker or earphones and a tactile signal from the electricshock of about 0.2 msec duration and about 12 mA of intensity deliveredto electrodes via the fingertip cot. Auditory clicks (e.g., about 100 dbSPL) may be delivered through a stethoscope earpiece by air conductiontubes from a magnetic speaker or other arrangement as would beunderstood by those skilled in the art. Ideally, these clicks may berectangular pulses of 1500 Hz tones at a repetition rate of about40/sec. The rate of stimulus may range between 7-50/second and may morepreferably range between 35-45/second (i.e., eliciting a 40 Hz auditorysteady-state evoked response (40 Hz) at an auditory tracer frequency 1(F1)).

The patient's brain responds to these stimuli, providing “EvokedPotentials” (EPs) which are averaged to reduce noise. Sample size varieswith stimulus modality, ranging from 100 (VEP) to 512-2048 (BAER/BSER).The average EP is the sum of samples time-locked to the onset of thestimuli divided by the number of samples, to provide an updated average.

The software program provides patient information. Typically, thepatient header gives the patient's IDS number, age and the date of theoperation. In addition, it may contain the name of the physician,anesthetist or other operator and the nature of the procedure. The timeis provided by a time code generator, which records both local time andelapsed time directly on the EEG tracings, so that events may beretrieved from any acquisition session given input to the database ofthe date. Retrieved data should include all clinical protocols andphysiological documentation, including the trajectories of the indices.The software program provides the data analysis module, described indetail elsewhere in this application.

After analysis of the data, the microprocessor 12 provides informationto the display 15 which informs the anesthesiologist or medicalpersonnel of the state of the patient with respect to the 3 dimensionsbeing monitored. This data can then be used to guide manualadministration of agents in accordance with the clinical judgment of thephysician. Alternatively, this information may be provided as controlsignals to a delivery control 19, which, automatically controls threeagent infusion pumps 22 a-22 c (e.g., Pumps A, B and C) to achieve adesired balance of the three agents. If the anesthetic is gaseous, theanesthesia control 19 may control valves of gas cylinders (not shown) aswould be understood by those of skill in the art.

Each of the three state indices (i.e., the DI, the MI and the PI) areseparately analyzed by the computer software of the present invention,in the frequency domain and also in the time domain.

The following is a preferred exemplary method for frequency domainanalysis of the depth of anesthesia to obtain the DI. The frequencydomain, from 0-200 Hz, is divided into narrow bins (e.g., for 0.50 Hzbins, 400 bins are set), QEEG variables are extracted from 0.05-1.5 Hz(low delta) to gamma 2 (35-50 Hz). Based upon experience (e.g., based ondata from sets of prior patients), features are selected from the datafrom univariate (i.e., single electrode) and multi-variate (i.e.,composite sets of electrodes) measures.

The data, in the frequency domain, is preferably converted by a FastFourier Transform (FFT) and then may be converted again by an InverseFast Fourier Transform (IFFT). The FFT is the preferred method forcalculating a power spectrum of the patients' brain waves. Using theFourier transformation, the complex wave diagram of the EEG is dividedinto underlying oscillation components, followed by a translation fromthe time domain into the frequency domain. The squared amplitudes ofthese oscillation components form the “power spectrum.” Furtherprocessing of the results of the Fourier analysis may include theextraction of spectrum parameters as well as continued statisticalcalculations. IFFT may be performed after analysis of the relative phasevariances at each frequency, of segments containing EP signals andsegments containing only noise samples, removing noise by settingappropriate coefficients to zero and reconstructing EPs with the noisedigitally removed. Parameters which may be derived from the spectrum,include, for example, the total power and absolute and relative power indifferent frequency bands. The median, the spectral edge frequency, andthe dominant frequency may also be used as parameters. The medianfrequency is most often defined as the 95% quantile (i.e., 95% of thetotal power of the spectrum is below this frequency). The dominantfrequency is the frequency with the highest power. Mean powers withinselected band intervals are calculated, transformed to achieve a normalor Gaussian distribution. Mean values and standard deviations of abaseline set of samples of each QEEG or EP variable are obtained duringan adequate and appropriate reference period to define the patients“self-norm.” The relevant population norm is obtained (i.e., from alook-up normative table). Z-scores are calculated for each univariate ormulti-variate QEEG or EP measure, relative to both the self- andpopulation-norms. For each variable, a sliding window, for example, 20seconds of data which is continuously updated, is formed whichintegrates sequential segments (i.e., 2.5 second artifact-free EEGsamples). From the updated mean value of the sliding window, thetrajectory of each variable and the DI, PI and MI are calculated. Thetrajectory of each index is presented to the physician as a quantitativemonitor of each dimension of patient state to provide guidance for theoptimal management of the patient or optionally may be used toautomatically control the delivery of various agents to the patient,preceded or accompanied by display of the intended maneuver to theoperator.

The EP wave shapes are stored. The peaks of the EPs are detected and anEP Index (“EPI”) is computed. The EPI reflects the area under the EPcurve, the length of the contour of the EP wave shape (“string length”),its peak amplitudes and its latencies. An updating EP waterfall typedisplay may be computed (Compressed EP Array, or CEPA), that scrolls(with time) with the EP peaks marked, for example, as brightened pointsor by arrows or stars. The automatic comb filter, mentioned above, maybe used to define an optimum digital filter for computation of any EPs .As in the case of the BAER/AER, for all EPs, the data is stored, thepeaks are detected, and an EP Index (“EPI”) is computed. The EPIreflects the area under the EP, and its string length, peak amplitudesand latencies. An updating scrolling waterfall display may be computedand displayed (on the monitor) with the EP peaks marked. In addition,separate updated sliding windows of data may be computed and displayed(on the monitor) for the patient's vital signs. Preferably, these vitalsigns include heart functions, as detected by QRS peaks, heart ratevariability, respiratory cycle, BP (Blood Pressure), oxygen saturation,and temperature. These vital signs windows are computed using the meansvalue and standard deviation for each of the vital signs.

The data collected and analyzed in the time domain and frequency domainare used to form the patient's multiple indices. These are, preferably,the Depth Index (PSI) (assesses anesthesia level), the Memory Index (MI)and the Pain Index (PI). A preferred method of computing these indicesmay be to use discriminant analysis as described in U.S. Pat. No.5,083,571 relating to psychiatric classification of a patient withrespect to a class or specific disorder and the inventor's prior U.S.Pat. No. 6,016,444. In general, discriminant analysis uses “discriminantfunctions”. U.S. Pat. Nos. 5,083,571 and 6,016,444 are hereby expresslyincorporated by reference in their entireties.

A discriminant function is composed of weighted combinations of subsetsof variables. In the case where patients'norms are used, the subsets areZ scores. Each of the subsets (each Z score) is selected, on the basisof experience and experimentation, because it significantly contributesto the discrimination (e.g., discrimination between anesthetized and yetfeeling pain and anesthetized and not feeling pain). The weighting ofthe subsets, the contribution of each Z score toward the discrimination,is also based on experience and experimentation.

The distributions of features of two groups of subjects, where thegroups belong to different diagnostic categories, may be thought of astwo clouds of points in a multidimensional space in which each dimensioncorresponds to a feature. For example, each feature is a Z score and thediagnostic categories, for example, are the degrees of anesthetizationto prevent pain. There may be no significant differences between twogroups (i.e., significant differences in other dimensions). A problemarises when these clouds of points overlap, i.e., when there is noapparent significant difference between two groups with respect to somefeatures. A solution is to attempt to define a boundary, through theclouds of points, to create a first zone which includes as much aspracticable of the first group, and as little as possible of the secondgroup, and a second zone which includes as much as practicable of thesecond group and as little as practicable of the first group.

The third zone is defined as an overlap region where no reliableclassification can be made. In principle, a discriminant functionweights the values of selected features for a new patient and adds theseweighted values to specify a single point in the relevantmultidimensional space. This single point then would be in one of thethree zones, and the individual's category (in each of the DI, MI andPI) would be classified accordingly. The discriminant analysis isperformed during the operation using Z scores based on self-norms (i.e.,comparison with the same patient pre-operation) and population norms(i.e., patients of the same age and condition during similar operationsusing the same anesthetic). After the DI, MI and PI are computed, thesecomputations may be used to automatically administer the variousanesthetics to the patient. In addition, these indices are displayed tothe operator on, e.g., a monitor. They may also be recorded and printedout for analysis after the operation. The DI gives an assessment inQEEG, the depth of anesthesia, the Memory Index (MI) is obtained, inQEEG, by combining the F (AER) value with selected AER features (i.e.,assessment of reception). This should give an indication of thepatients'ability to comprehend speech, i.e., the conversation of thedoctors and nurses during the operation. The Pain Index (PI) is derivedfrom the F (SER) values and selected SER features. In addition, non-QEEGautonomic measures, which are responsive to pain, may be used andcomputed into the PSI, PI and MI.

In order for the tendencies of the DI, MI and PI to control and/ormonitor the quantities of the various anesthetics, each of which isprimarily directed to one of those indices, it is necessary to measurethe effects of those anesthetics on the individual patients. Populationnorms are based on gender, age, surgical procedure and specificanesthetics. However, individuals, due to their metabolisms and otherfactors, may react differently from the average patient of suchpopulation norms.

The preferred method of determining the correct anesthesia dosage foreach patient is to test the patient using the 2 or 3 differentanesthetic agents which will be used for the operation. This QEEG methodmay analyze the particular patient's brain wave reaction to eachanesthetic agents, one at a time, and derive a “transfer function” foreach patient, reflecting that patient's biochemical reactions to eachanesthetic agent. For example, one patient may require an injection of 5milliliters of an anesthetic agent to prevent a feeling of pain, asshown by his PI, while another patient may require twice that dosage toobtain the same effect. The transfer function is preferably updated atregular intervals (i.e., for example every 20 minutes) during theoperation, as it may change if the operation lasts longer than 15-20minutes.

A preferred method for calculating each of transfer function may includeperturbation analysis. After the patient has been anesthetized to thedesired plane of anesthesia, and his QEEG self-norm (referenceset-point) has been obtained, the system halts delivery of the firstanesthetic, for example, the anesthesia remifentanyl primarily directedto control pain (PI) or may diminish the delivered amount by somefraction, for example, 50 percent. The patient then starts, in a gradualway, to show a change in the relevant index, e.g., DI, MI or PI. At aselected distance from a set point, preferably about 2.5 StandardDeviations (S.D.), (i.e., probability P<0.01), the application of theparticular anesthetic agent is resumed. The system, in this methoddetermines the number of anesthetic units withheld from the patient tocause the change in the relevant index. For example, it may requirewithholding 3 units for a particular patient to be roused to 2.5 S.D.from the set-point of the PI. The amount of anesthetic agent withheld,called the “test correction amount,” is an approximation of the amountof anesthetic agent required to restore that particular patient to theselected index when he has deviated from his set point by 2.5 S.D. Aselected fraction of that amount is then administered, as a firstapproximation, to see whether this restores the patient to the setpoint. The amount required to restore the patient to the set points isdefined as the “correction amount” and is retained in system memory andis administered to the patient whenever the patient has deviated fromhis set-point by 2.5 S.D. or more.

Adequate determination of the transfer function may require positive, aswell as negative perturbations. Preferably, periodic evaluation is shownby a significant change in each index (DI, MI and PI) caused by a smallincrement in the amount of anesthetic agent delivery, for example 10%.

As shown in FIG. 1, the anesthesia control 19, under management of themicroprocessor 12, controls the administration of the differentanesthetic agents. In this example, the anesthetic agents are injectionsand they are administered intravenously by infusion pumps A-C (22 a-22c). For example, pump A (22 a) may inject propofol (to control DI),while pump B (22 b) injects midzolam (to control MI) and pump C (22 c)injects remifentanyl (to control PI). Alternatively, as would beunderstood by those of skill in the art, any or all of the variousanesthetic agents may be gases which are administered through controlledinhalation by the patient.

For each index (DI, MI and PI), one infusion pump 22 a-22 c is used toadminister or withhold the anesthetic agent primarily directed towardcontrolling that index. For example, the DI is continually computed andcompared to the desired value or desired range of values. The pump(i.e., pump A (22 a)) is energized (or not energized) to inject (orwithhold) the corresponding anesthetic agent (e.g., propofol primarilyto control the PSI). Similarly, the MI and PI are continually computedand a corresponding one of the pumps 22 (i.e., pump B (22 b) or pump C(22 c), respectively) is controlled to inject, or withhold, theanesthetic agent primarily directed to that index.

As would be understood by those of skill in the art, the system of FIG.1 may be implemented incorporating a dedicated freestanding computer,such as a PC, a laptop or other handheld device. Alternatively, thecomputer and monitor portions (as distinct from the pumps) may beimplemented as part of a multi-modal monitor, which may also includesensors and displays of the patient's vital signs (i.e., blood pressure,respiration, O2 saturation, temperature and pulse (heart rate)). In anyevent, preferably, the display 15 is a monitor having a color screen todisplay graphics and alphanumerics. The operator control 17 maypreferably include a standard ASCI key board which may be used to enterthe patient header (e.g., name, age, gender, hospital number, date,medical procedure etc.) and comments (which may use function keys).Preferably, the display shows the results of the QEEG analysiscontinually during the operation. These displays may preferably include:

-   a) The trajectories of each of the indices (DI, MI and PI)    separately either on the same screen or in sequence;-   b) The set points and the selected ranges (permitted deviations) for    each index;-   c) The current numerical value for each index;-   d) A waterfall type display or/and actual (raw data) brain waves    showing, for each data channel, the FFT, AER and SER;-   e) Coded symbols and/or alarms for events which should be brought to    the attention of the operator, such as epileptic spikes, epileptic    seizures, burst suppression and abrupt changes in those vital signs    (e.g., BP, respiration, pulse (heart rate), O2 saturation and    temperature); and-   f) Wave shapes stored in an Epileptic form event file (epileptic    spikes and seizures) calculated and displayed as well as their    number and the their times of occurrences.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1. A method, comprising the steps of: administering a plurality ofinterventional agents to a patient until the patient attains apredetermined plane of anesthesia; amplifying and digitizing brain wavesof the patient before and after the administering step to generate afirst set of digital data; amplifying and digitizing brain waves of thepatient during a medical procedure to generate a second set of digitaldata; and computing separate trajectories for at least two differentindices of an anesthetic state of the patient during the medicalprocedure as a function of a comparison of the first and second sets ofdigital data, the indices including a Depth Index (DI), a Memory Index(MI) and a Pain Index (PI), wherein the DI corresponds to a depth ofanesthesia of the patient, the PI corresponds to a sensitivity of thepatient to pain and the MI corresponds to an ability of the patient toform and store memories.
 2. The method as in claim 1, further comprisingthe step of: controlling delivery of an amount of at least one of theinterventional agents as a function of the trajectories, wherein theinterventional agents include at least one of anesthetic, analgesic andamnesic agents.
 3. The method as in claim 1, further comprising the stepof: providing a control signal when any of the trajectories indicatesthat the patient is deviating from the selected plane of anesthesia. 4.The method as in claim 3, further comprising the step of: automatically,in response to the control signal, adjusting the amount of the at leastone of the interventional agents administered to the patient, whereinthe interventional agents include at least one of anesthetic, analgesicand amnesic agents.
 5. The method as in claim 2, wherein each of theinterventional agents is directed toward regulating a corresponding oneof the DI, the MI and the PI.
 6. The method as in claim 5, furthercomprising the step of: computing an adaptive transfer function for eachof the indices, wherein each transfer function reflects an effect oneach selected trajectory of at least one of an addition and a reductionof the delivery of the interventional agent directed primarily towardregulating the index corresponding to the selected trajectory.
 7. Themethod as in claim 1, further comprising the step of: displaying each ofthe trajectories.
 8. The method as in claim 1, further comprising thestep of: analyzing the first and second sets of digital data in at leastone of a time domain and a frequency domain.
 9. The method as in claim8, wherein the frequency domain is analyzed using at least one of a FastFourier Transform (FFT), an Inverse Fast Fourier Transform (IFF) and atime-frequency wavelet transform.
 10. The method as in claim 1, furthercomprising the step of: stimulating the patient to obtain evokedresponses both before and during the medical procedure, the stimulationsincluding at least one of an auditory stimulus and a somatosensorystimulus.
 11. The method as in claim 1, wherein the first set is asample of at least 2.5 seconds of artifact free data.
 12. The method asin claim 1, further comprising the step of: comparing the second set todata derived from a control group of patients who have undergone one ofthe same and a substantially similar medical procedure and used the sameinterventional agents.
 13. The method as in claim 5, wherein the amnesicagent relating to the MI is midazolam, the analgesic agent relating tothe PI is remifentanyl and the anesthetic agent relating to the DI ispropofol.
 14. A system, comprising: a delivery arrangement administeringa plurality of interventional agents to a patient; and a microprocessoranalyzing digital brain wave data corresponding to brain waves of thepatient, the microprocessor computing separate trajectories for at leasttwo different indices of an anesthetic state of the patient as afunction of the analysis of the digital brain wave data, the indicesincluding a Depth Index (DI), a Memory Index (MI) and a Pain Index (PI),the DI corresponding to a depth of anesthesia of the patient, the PIcorresponding to a sensitivity of the patient to pain and the MIcorresponding to an ability of the patient to form and store memories,wherein the microprocessor controls an amount of at least one of theinterventional agents as a function of the trajectories.
 15. The systemas in claim 14, further comprising: a plurality of electroencephalogram(EEG) electrodes coupled to a scalp of the patient and producingelectrical signals corresponding to brain waves of the patient.
 16. Thesystem as in claim 15, further comprising: amplifiers amplifying theelectrical signals.
 17. The system as in claim 16, further comprising:an analog-to-digital multiplexer converting the amplified electricalsignals into digital signals.
 18. The system as in claim 17, furthercomprising: a digital signal processor executing a signal processingalgorithm on the digital signals to generate the digital brain wavedata.
 19. A device, comprising: a communication arrangement coupled to adelivery arrangement administering a plurality of interventional agentsto a patient; and a microprocessor analyzing digital brain wave datacorresponding to brain waves of the patient, the microprocessorcomputing separate trajectories for at least two different indices of ananesthetic state of the patient during the medical procedure as afunction of the analysis of the digital brain wave data, the indicesincluding a Depth Index (DI), a Memory Index (MI) and a Pain Index (PI),the DI corresponding to a depth of anesthesia of the patient, the PIcorresponding to a sensitivity of the patient to pain and the MIcorresponding to an ability of the patient to form and store memories,wherein the microprocessor generates a control signal as a function ofthe trajectories, the control signal being transmitted to the deliveryarrangement via the communication arrangement to control an amount of atleast one of the interventional agents delivered to the patient.
 20. Amethod, comprising the steps of: amplifying and digitizing brain wavesof a patient to generate a first set of digital data, the patientreceiving a plurality of interventional agents; and computing separatetrajectories for at least two different indices of an anesthetic stateof the patient as a function of a comparison of the first set of digitaldata to a second set of digital data, the second set of digital dataincluding predetermined reference values for each of the at least twodifferent indices, the indices including a Depth Index (DI), a MemoryIndex (MI) and a Pain Index (PI), wherein the DI corresponds to a depthof anesthesia of the patient, the PI corresponds to a sensitivity of thepatient to pain and the MI corresponds to an ability of the patient toform and store memories.
 21. The method as in claim 20, furthercomprising the step of: providing a control signal when any of thetrajectories indicates that the patient is deviating from thepredetermined reference values.
 22. The method as in claim 21, furthercomprising the step of: transmitting the control signal to a computingdevice monitoring the trajectories.
 23. The method as in claim 20,wherein the interventional agents correcting digression from thepredetermined reference value for a corresponding one of the indices,the administration of each of the interventional agents being controlledbased on the trajectory corresponding to the index.
 24. The method as inclaim 23, wherein the trajectories are computed for each of the DI, theMI and the PI.
 25. The method as in claim 24, further comprising thestep of: computing an adaptive transfer function for each of theindices, each transfer function reflecting effects on each selectedtrajectory of at least one of addition and reduction of the delivery ofthe corresponding interventional agent.
 26. The method as in claim 20,further comprising the step of: displaying each of the trajectories. 27.The method as in claim 20, further comprising the step of: analyzing thefirst set of digital data in at least one of a time domain and afrequency domain.
 28. The method as in claim 27, wherein the frequencydomain is analyzed using at least one of a Fast Fourier Transform (FFT),an Inverse Fast Fourier Transform (IFFT) and a time-frequency wavelettransform.
 29. The method as in claim 27, further comprising the stepof: stimulating the patient to obtain evoked responses, the stimulationsincluding at least one of an auditory stimulus and a somatosensorystimulus.
 30. The method as in claim 20, further comprising the step of:computing the indices as a function of the first set and second sets ofdigital data, the predetermined reference values being at least one of(i) data representing a self-norm of the patient and (ii) datarepresenting a population norm for patients who have utilized the sameinterventional agents.